runspatialdecon,NanoStringGeoMxSet-method {SpatialDecon} | R Documentation

## Run spatialdecon on a NanostringGeomxSet object

### Description

A wrapper for applying spatialdecon to a NanostringGeomxSet object.

### Usage

    ## S4 method for signature 'NanoStringGeoMxSet'
    runspatialdecon(
      object,
      X = NULL,
      norm_elt = NULL,
      raw_elt = NULL,
      wts = NULL,
      resid_thresh = 3,
      lower_thresh = 0.5,
      align_genes = TRUE,
      is_pure_tumor = NULL,
      n_tumor_clusters = 10,
      cell_counts = NULL,
      cellmerges = NULL,
      maxit = 1000
    )

### Arguments

`object` | A NanostringGeomxSet object.
`X` | Cell profile matrix. If NULL, the safeTME matrix is used.
`norm_elt` | normalized data element in assayData
`raw_elt` | raw data element in assayData
`wts` | Optional, a matrix of weights.
`resid_thresh` | A scalar, sets a threshold on how extreme individual data points' values can be (in log2 units) before getting flagged as outliers and set to NA.
`lower_thresh` | A scalar. Before log2-scale residuals are calculated, both observed and fitted values get thresholded up to this value. Prevents log2-scale residuals from becoming extreme in points near zero.
`align_genes` | Logical. If TRUE, then Y, X, bg, and wts are row-aligned by shared genes.
`is_pure_tumor` | A logical vector denoting whether each AOI consists of pure tumor. If specified, then the algorithm will derive a tumor expression profile and merge it with the immune profiles matrix.
`n_tumor_clusters` | Number of tumor-specific columns to merge into the cell profile matrix. Has an impact only when is_pure_tumor argument is used to indicate pure tumor AOIs. Takes this many clusters from the pure-tumor AOI data and gets the average expression profile in each cluster. Default 10.
`cell_counts` | Number of cells estimated to be within each sample. If provided alongside norm_factors, then the algorithm will additionally output cell abundance esimtates on the scale of cell counts.
`cellmerges` | A list object holding the mapping from beta's cell names to combined cell names. If left NULL, then defaults to a mapping of granular immune cell definitions to broader categories.
`maxit` | Maximum number of iterations. Default 1000.

### Value

if not given cellmerges and cell_counts, a valid GeoMx S4 object including the following items

- In pData

  - beta: matrix of cell abundance estimates, cells in rows and observations in columns

  - p: matrix of p-values for H0: beta == 0

  - t: matrix of t-statistics for H0: beta == 0

  - se: matrix of standard errors of beta values

  - prop_of_all: rescaling of beta to sum to 1 in each observation

  - prop_of_nontumor: rescaling of beta to sum to 1 in each observation, excluding tumor abundance estimates

  - sigmas: covariance matrices of each observation's beta estimates

- In assayData

  - yhat: a matrix of fitted values

  - resids: a matrix of residuals from the model fit. (log2(pmax(y, lower_thresh)) - log2(pmax(xb, lower_thresh))).

- In experimentData

  - SpatialDeconMatrix: the cell profile matrix used in the decon fit.

if given cellmerges, the valid GeoMx S4 object will additionally include the following items

- In pData

  - beta.granular: cell abundances prior to combining closely-related cell types

  - sigma.granular: sigmas prior to combining closely-related cell types

if given cell_counts, the valid GeoMx S4 object will additionally include the following items

- In pData

  - cell.counts: beta rescaled to estimate cell numbers, based on prop_of_all and nuclei count

if given both cellmerges and cell_counts, the valid GeoMx S4 object will additionally include the following items

- In pData

  - cell.counts.granular: cell.counts prior to combining closely-related cell types

### Examples

    library(GeomxTools)
    datadir <- system.file("extdata", "DSP_NGS_Example_Data", package = "GeomxTools")
    demoData <- readRDS(file.path(datadir, "/demoData.rds"))

    demoData <- shiftCountsOne(demoData)
    target_demoData <- aggregateCounts(demoData)

    target_demoData <- normalize(target_demoData, "quant")

    demoData <- runspatialdecon(object = target_demoData,
                                norm_elt = "exprs_norm",
                                raw_elt = "exprs")

[Package SpatialDecon version 1.4.3 Index]